Shared machine learning

ABSTRACT

A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

RELATED APPLICATION

This patent application claims priority to U.S. Application No.62/502,440, filed on May 5, 2017 and entitled “Machine Learning AutoCompletion of Fields”, the contents of which are entirely incorporatedherein by reference, as if fully set forth in this application.Additionally, this patent application claims priority to U.S.Application No. 62/517,719, filed on Jun. 9, 2017 and entitled “MachineLearning Pilot”, the contents of which are entirely incorporated hereinby reference, as if fully set forth in this application.

BACKGROUND

As an enterprise employs cloud-based network(s), such as remotely hostedservices managed by a third party, those cloud-based network(s) maystore data that is accessible by client devices on the enterprise'snetwork. In some cases, the enterprise may seek to evaluate this datafor various purposes. For example, the enterprise may seek to makevarious conclusions by evaluating the data, so as to help the enterpriseto better organize the information presented by the data, to derivepatterns from the data, to improve operational decisions, and/or improveworkflow within the enterprise, among other possibilities.

Generally, to help facilitate the process of evaluating the data, theenterprise could rely on machine learning (ML) software, which executesalgorithms that learn from and make predictions on data. Unfortunately,however, ML software could consume a high extent of the enterprise'scomputational resources and/or could be relatively costly for theenterprise to obtain.

SUMMARY

Disclosed herein is a cloud-based network system that provides a remoteML arrangement, which can be shared among various enterprise networks.The remote ML arrangement can securely generate ML model(s) andprediction(s) that are based on given enterprise's data and areaccessible only to client devices on the given enterprise's network. Inthis way, the network system could help an enterprise to save time, toimprove use of computing resources, and/or to reduce costs onspecialized software, among other possible outcomes.

More specifically, the network system may include a computing system anda plurality of trainer devices. Each trainer device may be configured toexecute one or more ML trainer processes that respectively generate MLmodel(s). The computing system may be configured to communicate with theenterprise network's client devices and to make an ML prediction basedon a generated ML model. In this way, a client device could communicatewith the computing system to effectively request the network system tocarry out a certain prediction.

When a client device submits such a request, the client device couldprovide certain information to the computing system. In particular, theprovided information could designate a portion of the enterprise's data(e.g., remotely stored at the computing system) as training data thatshould be used as basis for generating an ML model. Additionally, theprovided information could indicate a target variable to be predictedusing the ML model. For example, the client device could request thenetwork system to predict categories for any uncategorized informationwithin certain fields of a data table.

As such, once the computing system receives the information from theclient device, the computing system may transmit an ML training requestfor reception by one of the plurality of trainer devices. For example,the computing system may transmit that ML training request to ascheduler device, and the scheduler device may then assign the MLtraining request to be served by a particular one of the ML trainerprocesses, which is executable by a particular one of the ML trainerdevices. Once the ML training request has been assigned, the particularML trainer process may then serve that ML training request.

When the particular ML trainer process serves the ML training request,the particular ML trainer device may engage in various communicationswith the computing system. Specifically, the computing system mayprovide the training data to the particular ML trainer device executingthe particular ML trainer process. In practice, the computing system maydo so after engaging in an authentication process to verify that theparticular ML trainer process has permission to access that data,thereby securing the enterprise's data against unauthorized access.Moreover, once the ML model is generated based on the provided trainingdata and according to the particular ML trainer process, the particularML trainer device may then send the generated ML model to the computingsystem, and may also delete the training data stored at the particularML trainer device, which may further secure the enterprise's dataagainst unauthorized access.

Once the computing system receives the generated ML model from theparticular ML trainer device, the computing system may then predict thetarget variable using the ML model. In particular, the computing systemcould execute an ML prediction Application Programming Interface (API)to predict the target variable using the ML model. In this regard, giventhat the ML prediction occurs separately from the ML model generationand occurs at the computing system, the computing system could feasiblycarry out the prediction at any time once the computing system has theML model, even if the computing system doesn't have an establishednetwork connection with any one of the trainer devices. Moreover, thecomputing system could use that same ML model to carry out additionalprediction(s). Additionally or alternatively, the computing system couldobtain updated ML model(s) and could use those updated ML model(s) tocarry out additional prediction(s).

In any case, after the computing system carries out a prediction usingan ML model obtained from one of the ML trainer devices, the computingsystem could send, to a client device, information related to thatprediction. For example, the computing system could transmit informationindicating the target variable to the client device, such as by causinga web browser of the client device to display the information indicatingthe target variable. In this way, an enterprise could securely obtainuseful ML predictions without the enterprise having to dedicatesignificant computational resources for this purpose and without theenterprise having to invest in costly specialized software, among otheradvantages.

Accordingly, a first example embodiment may involve a network systemincluding a plurality of trainer devices disposed within a remotenetwork management platform and a computing system disposed within theremote network management platform. Each trainer device may beconfigured to execute one or more ML trainer processes. Additionally,the computing system may be configured to: receive informationindicating (i) training data that is associated with the computingsystem and that is to be used as basis for generating an ML model and(ii) a target variable to be predicted using the ML model, where theinformation is received from a client device of a managed network, andwhere the remote network management platform remotely manages themanaged network; transmit an ML training request for reception by one ofthe plurality of trainer devices, where the ML training request is basedon the received information; provide the training data to a particulartrainer device executing a particular ML trainer process that is servingthe ML training request; receive, from the particular trainer device,the ML model that is generated based on the provided training data andaccording to the particular ML trainer process; predict the targetvariable using the ML model; and transmit, to the client device,information indicating the target variable.

A second example embodiment may involve receiving, by a computing systemof a remote network management platform, information indicating (i)training data that is associated with the computing system and that isto be used as basis for generating an ML model and (ii) a targetvariable to be predicted using the ML model, where the information isreceived from a client device of a managed network, where the remotenetwork management platform remotely manages the managed network, wherea plurality of trainer devices are disposed within the remote networkmanagement platform, and where each trainer device is configured toexecute one or more ML trainer processes. The second example embodimentmay also involve transmitting, by the computing system, an ML trainingrequest for reception by one of the plurality of trainer devices, wherethe ML training request is based on the received information. The secondexample embodiment may additionally involve providing, by the computingsystem, the training data to a particular trainer device executing aparticular ML trainer process that is serving the ML training request.The second example embodiment may further involve receiving, by thecomputing system from the particular trainer device, the ML model thatis generated based on the provided training data and according to theparticular ML trainer process. The second example embodiment may yetfurther involve predicting, by the computing system, the target variableusing the ML model. The second example embodiment may yet furtherinvolve transmitting, by the computing system to the client device,information indicating the target variable.

In a third example embodiment, an article of manufacture may include anon-transitory computer-readable medium, having stored thereon programinstructions that, upon execution by a computing system, cause thecomputing system to perform operations in accordance with the firstand/or second example embodiment.

In a fourth example embodiment, a computing system may include at leastone processor, as well as memory and program instructions. The programinstructions may be stored in the memory, and upon execution by the atleast one processor, cause the computing system to perform operations inaccordance with the first and/or second example embodiment.

In a fifth example embodiment, a system may include various means forcarrying out each of the operations of the first and/or second exampleembodiment.

These as well as other embodiments, aspects, advantages, andalternatives will become apparent to those of ordinary skill in the artby reading the following detailed description, with reference whereappropriate to the accompanying drawings. Further, this summary andother descriptions and figures provided herein are intended toillustrate embodiments by way of example only and, as such, thatnumerous variations are possible. For instance, structural elements andprocess steps can be rearranged, combined, distributed, eliminated, orotherwise changed, while remaining within the scope of the embodimentsas claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic drawing of a computing device, inaccordance with example embodiments.

FIG. 2 illustrates a schematic drawing of a server device cluster, inaccordance with example embodiments.

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments.

FIG. 4 depicts a communication environment involving a remote networkmanagement architecture, in accordance with example embodiments.

FIG. 5A depicts another communication environment involving a remotenetwork management architecture, in accordance with example embodiments.

FIG. 5B is a flow chart, in accordance with example embodiments.

FIG. 6 depicts communication between a client device, a computingsystem, a scheduler device, and a trainer device, in accordance withexample embodiments.

FIG. 7 is a flow chart, in accordance with example embodiments.

DETAILED DESCRIPTION

Example methods, devices, and systems are described herein. It should beunderstood that the words “example” and “exemplary” are used herein tomean “serving as an example, instance, or illustration.” Any embodimentor feature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features unless stated as such. Thus, other embodimentscan be utilized and other changes can be made without departing from thescope of the subject matter presented herein.

Accordingly, the example embodiments described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations. For example, theseparation of features into “client” and “server” components may occurin a number of ways.

Further, unless context suggests otherwise, the features illustrated ineach of the figures may be used in combination with one another. Thus,the figures should be generally viewed as component aspects of one ormore overall embodiments, with the understanding that not allillustrated features are necessary for each embodiment.

Additionally, any enumeration of elements, blocks, or steps in thisspecification or the claims is for purposes of clarity. Thus, suchenumeration should not be interpreted to require or imply that theseelements, blocks, or steps adhere to a particular arrangement or arecarried out in a particular order.

I. INTRODUCTION

A large enterprise is a complex entity with many interrelatedoperations. Some of these are found across the enterprise, such as humanresources (HR), supply chain, information technology (IT), and finance.However, each enterprise also has its own unique operations that provideessential capabilities and/or create competitive advantages.

To support widely-implemented operations, enterprises typically useoff-the-shelf software applications, such as customer relationshipmanagement (CRM) and human capital management (HCM) packages. However,they may also need custom software applications to meet their own uniquerequirements. A large enterprise often has dozens or hundreds of thesecustom software applications. Nonetheless, the advantages provided bythe embodiments herein are not limited to large enterprises and may beapplicable to an enterprise, or any other type of organization, of anysize.

Many such software applications are developed by individual departmentswithin the enterprise. These range from simple spreadsheets tocustom-built software tools and databases. But the proliferation ofsiloed custom software applications has numerous disadvantages. Itnegatively impacts an enterprise's ability to run and grow its business,innovate, and meet regulatory requirements. The enterprise may find itdifficult to integrate, streamline and enhance its operations due tolack of a single system that unifies its subsystems and data.

To efficiently create custom applications, enterprises would benefitfrom a remotely-hosted application platform that eliminates unnecessarydevelopment complexity. The goal of such a platform would be to reducetime-consuming, repetitive application development tasks so thatsoftware engineers and individuals in other roles can focus ondeveloping unique, high-value features.

In order to achieve this goal, the concept of Application Platform as aService (aPaaS) is introduced, to intelligently automate workflowsthroughout the enterprise. An aPaaS system is hosted remotely from theenterprise, but may access data, applications, and services within theenterprise by way of secure connections. Such an aPaaS system may have anumber of advantageous capabilities and characteristics. Theseadvantages and characteristics may be able to improve the enterprise'soperations and workflow for IT, HR, CRM, customer service, applicationdevelopment, and security.

The aPaaS system may support development and execution ofmodel-view-controller (MVC) applications. MVC applications divide theirfunctionality into three interconnected parts (model, view, andcontroller) in order to isolate representations of information from themanner in which the information is presented to the user, therebyallowing for efficient code reuse and parallel development. Theseapplications may be web-based, and offer create, read, update, delete(CRUD) capabilities. This allows new applications to be built on acommon application infrastructure.

The aPaaS system may support standardized application components, suchas a standardized set of widgets for graphical user interface (GUI)development. In this way, applications built using the aPaaS system havea common look and feel. Other software components and modules may bestandardized as well. In some cases, this look and feel can be brandedor skinned with an enterprise's custom logos and/or color schemes.

The aPaaS system may support the ability to configure the behavior ofapplications using metadata. This allows application behaviors to berapidly adapted to meet specific needs. Such an approach reducesdevelopment time and increases flexibility. Further, the aPaaS systemmay support GUI tools that facilitate metadata creation and management,thus reducing errors in the metadata.

The aPaaS system may support clearly-defined interfaces betweenapplications, so that software developers can avoid unwantedinter-application dependencies. Thus, the aPaaS system may implement aservice layer in which persistent state information and other data isstored.

The aPaaS system may support a rich set of integration features so thatthe applications thereon can interact with legacy applications andthird-party applications. For instance, the aPaaS system may support acustom employee-onboarding system that integrates with legacy HR, IT,and accounting systems.

The aPaaS system may support enterprise-grade security. Furthermore,since the aPaaS system may be remotely hosted, it should also utilizesecurity procedures when it interacts with systems in the enterprise orthird-party networks and services hosted outside of the enterprise. Forexample, the aPaaS system may be configured to share data amongst theenterprise and other parties to detect and identify common securitythreats.

Other features, functionality, and advantages of an aPaaS system mayexist. This description is for purpose of example and is not intended tobe limiting.

As an example of the aPaaS development process, a software developer maybe tasked to create a new application using the aPaaS system. First, thedeveloper may define the data model, which specifies the types of datathat the application uses and the relationships therebetween. Then, viaa GUI of the aPaaS system, the developer enters (e.g., uploads) the datamodel. The aPaaS system automatically creates all of the correspondingdatabase tables, fields, and relationships, which can then be accessedvia an object-oriented services layer.

In addition, the aPaaS system can also build a fully-functional MVCapplication with client-side interfaces and server-side CRUD logic. Thisgenerated application may serve as the basis of further development forthe user. Advantageously, the developer does not have to spend a largeamount of time on basic application functionality. Further, since theapplication may be web-based, it can be accessed from anyInternet-enabled client device. Alternatively or additionally, a localcopy of the application may be able to be accessed, for instance, whenInternet service is not available.

The aPaaS system may also support a rich set of pre-definedfunctionality that can be added to applications. These features includesupport for searching, email, templating, workflow design, reporting,analytics, social media, scripting, mobile-friendly output, andcustomized GUIs.

The following embodiments describe architectural and functional aspectsof example aPaaS systems, as well as the features and advantagesthereof.

II. EXAMPLE COMPUTING DEVICES AND CLOUD-BASED COMPUTING ENVIRONMENTS

FIG. 1 is a simplified block diagram exemplifying a computing device100, illustrating some of the components that could be included in acomputing device arranged to operate in accordance with the embodimentsherein. Computing device 100 could be a client device (e.g., a deviceactively operated by a user), a server device (e.g., a device thatprovides computational services to client devices), or some other typeof computational platform. Some server devices may operate as clientdevices from time to time in order to perform particular operations, andsome client devices may incorporate server features.

In this example, computing device 100 includes processor 102, memory104, network interface 106, and an input/output unit 108, all of whichmay be coupled by a system bus 110 or a similar mechanism. In someembodiments, computing device 100 may include other components and/orperipheral devices (e.g., detachable storage, printers, and so on).

Processor 102 may be one or more of any type of computer processingelement, such as a central processing unit (CPU), a co-processor (e.g.,a mathematics, graphics, or encryption co-processor), a digital signalprocessor (DSP), a network processor, and/or a form of integratedcircuit or controller that performs processor operations. In some cases,processor 102 may be one or more single-core processors. In other cases,processor 102 may be one or more multi-core processors with multipleindependent processing units. Processor 102 may also include registermemory for temporarily storing instructions being executed and relateddata, as well as cache memory for temporarily storing recently-usedinstructions and data.

Memory 104 may be any form of computer-usable memory, including but notlimited to random access memory (RAM), read-only memory (ROM), andnon-volatile memory (e.g., flash memory, hard disk drives, solid statedrives, compact discs (CDs), digital video discs (DVDs), and/or tapestorage). Thus, memory 104 represents both main memory units, as well aslong-term storage. Other types of memory may include biological memory.

Memory 104 may store program instructions and/or data on which programinstructions may operate. By way of example, memory 104 may store theseprogram instructions on a non-transitory, computer-readable medium, suchthat the instructions are executable by processor 102 to carry out anyof the methods, processes, or operations disclosed in this specificationor the accompanying drawings.

As shown in FIG. 1, memory 104 may include firmware 104A, kernel 104B,and/or applications 104C. Firmware 104A may be program code used to bootor otherwise initiate some or all of computing device 100. Kernel 104Bmay be an operating system, including modules for memory management,scheduling and management of processes, input/output, and communication.Kernel 104B may also include device drivers that allow the operatingsystem to communicate with the hardware modules (e.g., memory units,networking interfaces, ports, and busses), of computing device 100.Applications 104C may be one or more user-space software programs, suchas web browsers or email clients, as well as any software libraries usedby these programs. Memory 104 may also store data used by these andother programs and applications.

Network interface 106 may take the form of one or more wirelineinterfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, andso on). Network interface 106 may also support communication over one ormore non-Ethernet media, such as coaxial cables or power lines, or overwide-area media, such as Synchronous Optical Networking (SONET) ordigital subscriber line (DSL) technologies. Network interface 106 mayadditionally take the form of one or more wireless interfaces, such asIEEE 802.11 (Wifi), BLUETOOTH®, global positioning system (GPS), or awide-area wireless interface. However, other forms of physical layerinterfaces and other types of standard or proprietary communicationprotocols may be used over network interface 106. Furthermore, networkinterface 106 may comprise multiple physical interfaces. For instance,some embodiments of computing device 100 may include Ethernet,BLUETOOTH®, and Wifi interfaces.

Input/output unit 108 may facilitate user and peripheral deviceinteraction with example computing device 100. Input/output unit 108 mayinclude one or more types of input devices, such as a keyboard, a mouse,a touch screen, and so on. Similarly, input/output unit 108 may includeone or more types of output devices, such as a screen, monitor, printer,and/or one or more light emitting diodes (LEDs). Additionally oralternatively, computing device 100 may communicate with other devicesusing a universal serial bus (USB) or high-definition multimediainterface (HDMI) port interface, for example.

In some embodiments, one or more instances of computing device 100 maybe deployed to support an aPaaS architecture. The exact physicallocation, connectivity, and configuration of these computing devices maybe unknown and/or unimportant to client devices. Accordingly, thecomputing devices may be referred to as “cloud-based” devices that maybe housed at various remote data center locations.

FIG. 2 depicts a cloud-based server cluster 200 in accordance withexample embodiments. In FIG. 2, operations of a computing device (e.g.,computing device 100) may be distributed between server devices 202,data storage 204, and routers 206, all of which may be connected bylocal cluster network 208. The number of server devices 202, datastorages 204, and routers 206 in server cluster 200 may depend on thecomputing task(s) and/or applications assigned to server cluster 200.

For example, server devices 202 can be configured to perform variouscomputing tasks of computing device 100. Thus, computing tasks can bedistributed among one or more of server devices 202. To the extent thatthese computing tasks can be performed in parallel, such a distributionof tasks may reduce the total time to complete these tasks and return aresult. For purpose of simplicity, both server cluster 200 andindividual server devices 202 may be referred to as a “server device.”This nomenclature should be understood to imply that one or moredistinct server devices, data storage devices, and cluster routers maybe involved in server device operations.

Data storage 204 may be data storage arrays that include drive arraycontrollers configured to manage read and write access to groups of harddisk drives and/or solid state drives. The drive array controllers,alone or in conjunction with server devices 202, may also be configuredto manage backup or redundant copies of the data stored in data storage204 to protect against drive failures or other types of failures thatprevent one or more of server devices 202 from accessing units ofcluster data storage 204. Other types of memory aside from drives may beused.

Routers 206 may include networking equipment configured to provideinternal and external communications for server cluster 200. Forexample, routers 206 may include one or more packet-switching and/orrouting devices (including switches and/or gateways) configured toprovide (i) network communications between server devices 202 and datastorage 204 via cluster network 208, and/or (ii) network communicationsbetween the server cluster 200 and other devices via communication link210 to network 212.

Additionally, the configuration of cluster routers 206 can be based atleast in part on the data communication requirements of server devices202 and data storage 204, the latency and throughput of the localcluster network 208, the latency, throughput, and cost of communicationlink 210, and/or other factors that may contribute to the cost, speed,fault-tolerance, resiliency, efficiency and/or other design goals of thesystem architecture.

As a possible example, data storage 204 may include any form ofdatabase, such as a structured query language (SQL) database. Varioustypes of data structures may store the information in such a database,including but not limited to tables, arrays, lists, trees, and tuples.Furthermore, any databases in data storage 204 may be monolithic ordistributed across multiple physical devices.

Server devices 202 may be configured to transmit data to and receivedata from cluster data storage 204. This transmission and retrieval maytake the form of SQL queries or other types of database queries, and theoutput of such queries, respectively. Additional text, images, video,and/or audio may be included as well. Furthermore, server devices 202may organize the received data into web page representations. Such arepresentation may take the form of a markup language, such as thehypertext markup language (HTML), the extensible markup language (XML),or some other standardized or proprietary format. Moreover, serverdevices 202 may have the capability of executing various types ofcomputerized scripting languages, such as but not limited to Perl,Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP),JavaScript, and so on. Computer program code written in these languagesmay facilitate the providing of web pages to client devices, as well asclient device interaction with the web pages.

III. EXAMPLE REMOTE NETWORK MANAGEMENT ARCHITECTURE

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments. This architecture includes three maincomponents, managed network 300, remote network management platform 320,and third-party networks 340, all connected by way of Internet 350.

Managed network 300 may be, for example, an enterprise network used by abusiness for computing and communications tasks, as well as storage ofdata. Thus, managed network 300 may include various client devices 302,server devices 304, routers 306, virtual machines 308, firewall 310,and/or proxy servers 312. Client devices 302 may be embodied bycomputing device 100, server devices 304 may be embodied by computingdevice 100 or server cluster 200, and routers 306 may be any type ofrouter, switch, or gateway.

Virtual machines 308 may be embodied by one or more of computing device100 or server cluster 200. In general, a virtual machine is an emulationof a computing system, and mimics the functionality (e.g., processor,memory, and communication resources) of a physical computer. Onephysical computing system, such as server cluster 200, may support up tothousands of individual virtual machines. In some embodiments, virtualmachines 308 may be managed by a centralized server device orapplication that facilitates allocation of physical computing resourcesto individual virtual machines, as well as performance and errorreporting. Enterprises often employ virtual machines in order toallocate computing resources in an efficient, as needed fashion.Providers of virtualized computing systems include VMWARE® andMICROSOFT®.

Firewall 310 may be one or more specialized routers or server devicesthat protect managed network 300 from unauthorized attempts to accessthe devices, applications, and services therein, while allowingauthorized communication that is initiated from managed network 300.Firewall 310 may also provide intrusion detection, web filtering, virusscanning, application-layer gateways, and other applications orservices. In some embodiments not shown in FIG. 3, managed network 300may include one or more virtual private network (VPN) gateways withwhich it communicates with remote network management platform 320 (seebelow).

Managed network 300 may also include one or more proxy servers 312. Anembodiment of proxy servers 312 may be a server device that facilitatescommunication and movement of data between managed network 300, remotenetwork management platform 320, and third-party networks 340. Inparticular, proxy servers 312 may be able to establish and maintainsecure communication sessions with one or more customer instances ofremote network management platform 320. By way of such a session, remotenetwork management platform 320 may be able to discover and manageaspects of the architecture and configuration of managed network 300 andits components. Possibly with the assistance of proxy servers 312,remote network management platform 320 may also be able to discover andmanage aspects of third-party networks 340 that are used by managednetwork 300.

Firewalls, such as firewall 310, typically deny all communicationsessions that are incoming by way of Internet 350, unless such a sessionwas ultimately initiated from behind the firewall (i.e., from a deviceon managed network 300) or the firewall has been explicitly configuredto support the session. By placing proxy servers 312 behind firewall 310(e.g., within managed network 300 and protected by firewall 310), proxyservers 312 may be able to initiate these communication sessions throughfirewall 310. Thus, firewall 310 might not have to be specificallyconfigured to support incoming sessions from remote network managementplatform 320, thereby avoiding potential security risks to managednetwork 300.

In some cases, managed network 300 may consist of a few devices and asmall number of networks. In other deployments, managed network 300 mayspan multiple physical locations and include hundreds of networks andhundreds of thousands of devices. Thus, the architecture depicted inFIG. 3 is capable of scaling up or down by orders of magnitude.

Furthermore, depending on the size, architecture, and connectivity ofmanaged network 300, a varying number of proxy servers 312 may bedeployed therein. For example, each one of proxy servers 312 may beresponsible for communicating with remote network management platform320 regarding a portion of managed network 300. Alternatively oradditionally, sets of two or more proxy servers may be assigned to sucha portion of managed network 300 for purposes of load balancing,redundancy, and/or high availability.

Remote network management platform 320 is a hosted environment thatprovides aPaaS services to users, particularly to the operators ofmanaged network 300. These services may take the form of web-basedportals, for instance. Thus, a user can securely access remote networkmanagement platform 320 from, for instance, client devices 302, orpotentially from a client device outside of managed network 300. By wayof the web-based portals, users may design, test, and deployapplications, generate reports, view analytics, and perform other tasks.

As shown in FIG. 3, remote network management platform 320 includes fourcustomer instances 322, 324, 326, and 328. Each of these instances mayrepresent a set of web portals, services, and applications (e.g., awholly-functioning aPaaS system) available to a particular customer. Insome cases, a single customer may use multiple customer instances. Forexample, managed network 300 may be an enterprise customer of remotenetwork management platform 320, and may use customer instances 322,324, and 326. The reason for providing multiple instances to onecustomer is that the customer may wish to independently develop, test,and deploy its applications and services. Thus, customer instance 322may be dedicated to application development related to managed network300, customer instance 324 may be dedicated to testing theseapplications, and customer instance 326 may be dedicated to the liveoperation of tested applications and services.

The multi-instance architecture of remote network management platform320 is in contrast to conventional multi-tenant architectures, overwhich multi-instance architectures have several advantages. Inmulti-tenant architectures, data from different customers (e.g.,enterprises) are comingled in a single database. While these customers'data are separate from one another, the separation is enforced by thesoftware that operates the single database. As a consequence, a securitybreach in this system may impact all customers' data, creatingadditional risk, especially for entities subject to governmental,healthcare, and/or financial regulation. Furthermore, any databaseoperations that impact one customer will likely impact all customerssharing that database. Thus, if there is an outage due to hardware orsoftware errors, this outage affects all such customers. Likewise, ifthe database is to be upgraded to meet the needs of one customer, itwill be unavailable to all customers during the upgrade process. Often,such maintenance windows will be long, due to the size of the shareddatabase

In contrast, the multi-instance architecture provides each customer withits own database in a dedicated computing instance. This preventscomingling of customer data, and allows each instance to beindependently managed. For example, when one customer's instanceexperiences an outage due to errors or an upgrade, other customerinstances are not impacted. Maintenance down time is limited because thedatabase only contains one customer's data. Further, the simpler designof the multi-instance architecture allows redundant copies of eachcustomer database and instance to be deployed in a geographicallydiverse fashion. This facilitates high availability, where the liveversion of the customer's instance can be moved when faults are detectedor maintenance is being performed.

In order to support multiple customer instances in an efficient fashion,remote network management platform 320 may implement a plurality ofthese instances on a single hardware platform. For example, when theaPaaS system is implemented on a server cluster such as server cluster200, it may operate a virtual machine that dedicates varying amounts ofcomputational, storage, and communication resources to instances. Butfull virtualization of server cluster 200 might not be necessary, andother mechanisms may be used to separate instances. In some examples,each instance may have a dedicated account and one or more dedicateddatabases on server cluster 200. Alternatively, customer instance 322may span multiple physical devices.

In some cases, a single server cluster of remote network managementplatform 320 may support multiple independent enterprises. Furthermore,as described below, remote network management platform 320 may includemultiple server clusters deployed in geographically diverse data centersin order to facilitate load balancing, redundancy, and/or highavailability.

Third-party networks 340 may be remote server devices (e.g., a pluralityof server clusters such as server cluster 200) that can be used foroutsourced computational, data storage, communication, and servicehosting operations. These servers may be virtualized (i.e., the serversmay be virtual machines). Examples of third-party networks 340 mayinclude AMAZON WEB SERVICES® and MICROSOFT® Azure. Like remote networkmanagement platform 320, multiple server clusters supporting third-partynetworks 340 may be deployed at geographically diverse locations forpurposes of load balancing, redundancy, and/or high availability.

Managed network 300 may use one or more of third-party networks 340 todeploy applications and services to its clients and customers. Forinstance, if managed network 300 provides online music streamingservices, third-party networks 340 may store the music files and provideweb interface and streaming capabilities. In this way, the enterprise ofmanaged network 300 does not have to build and maintain its own serversfor these operations.

Remote network management platform 320 may include modules thatintegrate with third-party networks 340 to expose virtual machines andmanaged services therein to managed network 300. The modules may allowusers to request virtual resources and provide flexible reporting forthird-party networks 340. In order to establish this functionality, auser from managed network 300 might first establish an account withthird-party networks 340, and request a set of associated resources.Then, the user may enter the account information into the appropriatemodules of remote network management platform 320. These modules maythen automatically discover the manageable resources in the account, andalso provide reports related to usage, performance, and billing.

Internet 350 may represent a portion of the global Internet. However,Internet 350 may alternatively represent a different type of network,such as a private wide-area or local-area packet-switched network.

FIG. 4 further illustrates the communication environment between managednetwork 300 and customer instance 322, and introduces additionalfeatures and alternative embodiments. In FIG. 4, customer instance 322is replicated across data centers 400A and 400B. These data centers maybe geographically distant from one another, perhaps in different citiesor different countries. Each data center includes support equipment thatfacilitates communication with managed network 300, as well as remoteusers.

In data center 400A, network traffic to and from external devices flowseither through VPN gateway 402A or firewall 404A. VPN gateway 402A maybe peered with VPN gateway 412 of managed network 300 by way of asecurity protocol such as Internet Protocol Security (IPSEC). Firewall404A may be configured to allow access from authorized users, such asuser 414 and remote user 416, and to deny access to unauthorized users.By way of firewall 404A, these users may access customer instance 322,and possibly other customer instances. Load balancer 406A may be used todistribute traffic amongst one or more physical or virtual serverdevices that host customer instance 322. Load balancer 406A may simplifyuser access by hiding the internal configuration of data center 400A,(e.g., customer instance 322) from client devices. For instance, ifcustomer instance 322 includes multiple physical or virtual computingdevices that share access to multiple databases, load balancer 406A maydistribute network traffic and processing tasks across these computingdevices and databases so that no one computing device or database issignificantly busier than the others. In some embodiments, customerinstance 322 may include VPN gateway 402A, firewall 404A, and loadbalancer 406A.

Data center 400B may include its own versions of the components in datacenter 400A. Thus, VPN gateway 402B, firewall 404B, and load balancer406B may perform the same or similar operations as VPN gateway 402A,firewall 404A, and load balancer 406A, respectively. Further, by way ofreal-time or near-real-time database replication and/or otheroperations, customer instance 322 may exist simultaneously in datacenters 400A and 400B.

Data centers 400A and 400B as shown in FIG. 4 may facilitate redundancyand high availability. In the configuration of FIG. 4, data center 400Ais active and data center 400B is passive. Thus, data center 400A isserving all traffic to and from managed network 300, while the versionof customer instance 322 in data center 400B is being updated innear-real-time. Other configurations, such as one in which both datacenters are active, may be supported.

Should data center 400A fail in some fashion or otherwise becomeunavailable to users, data center 400B can take over as the active datacenter. For example, domain name system (DNS) servers that associate adomain name of customer instance 322 with one or more Internet Protocol(IP) addresses of data center 400A may re-associate the domain name withone or more IP addresses of data center 400B. After this re-associationcompletes (which may take less than one second or several seconds),users may access customer instance 322 by way of data center 400B.

FIG. 4 also illustrates a possible configuration of managed network 300.As noted above, proxy servers 312 and user 414 may access customerinstance 322 through firewall 310. Proxy servers 312 may also accessconfiguration items 410. In FIG. 4, configuration items 410 may refer toany or all of client devices 302, server devices 304, routers 306, andvirtual machines 308, any applications or services executing thereon, aswell as relationships between devices, applications, and services. Thus,the term “configuration items” may be shorthand for any physical orvirtual device, or any application or service remotely discoverable ormanaged by customer instance 322, or relationships between discovereddevices, applications, and services. Configuration items may berepresented in a configuration management database (CMDB) of customerinstance 322.

As noted above, VPN gateway 412 may provide a dedicated VPN to VPNgateway 402A. Such a VPN may be helpful when there is a significantamount of traffic between managed network 300 and customer instance 322,or security policies otherwise suggest or require use of a VPN betweenthese sites. In some embodiments, any device in managed network 300and/or customer instance 322 that directly communicates via the VPN isassigned a public IP address. Other devices in managed network 300and/or customer instance 322 may be assigned private IP addresses (e.g.,IP addresses selected from the 10.0.0.0-10.255.255.255 or192.168.0.0-192.168.255.255 ranges, represented in shorthand as subnets10.0.0.0/8 and 192.168.0.0/16, respectively).

IV. EXAMPLE DEVICE, APPLICATION, AND SERVICE DISCOVERY

In order for remote network management platform 320 to administer thedevices applications, and services of managed network 300, remotenetwork management platform 320 may first determine what devices arepresent in managed network 300, the configurations and operationalstatuses of these devices, and the applications and services provided bythe devices, and well as the relationships between discovered devices,applications, and services. As noted above, each device, application,service, and relationship may be referred to as a configuration item.The process of defining configuration items within managed network 300is referred to as discovery, and may be facilitated at least in part byproxy servers 312.

For purpose of the embodiments herein, an “application” may refer to oneor more processes, threads, programs, client modules, server modules, orany other software that executes on a device or group of devices. A“service” may refer to a high-level capability provided by multipleapplications executing on one or more devices working in conjunctionwith one another. For example, a high-level web service may involvemultiple web application server threads executing on one device andaccessing information from a database application that executes onanother device.

FIG. 5A provides a logical depiction of how configuration items can bediscovered, as well as how information related to discoveredconfiguration items can be stored. For sake of simplicity, remotenetwork management platform 320, third-party networks 340, and Internet350 are not shown.

In FIG. 5A, CMDB 500 and task list 502 are stored within customerinstance 322. Customer instance 322 may transmit discovery commands toproxy servers 312. In response, proxy servers 312 may transmit probes tovarious devices, applications, and services in managed network 300.These devices, applications, and services may transmit responses toproxy servers 312, and proxy servers 312 may then provide informationregarding discovered configuration items to CMDB 500 for storagetherein. Configuration items stored in CMDB 500 represent theenvironment of managed network 300.

Task list 502 represents a list of activities that proxy servers 312 areto perform on behalf of customer instance 322. As discovery takes place,task list 502 is populated. Proxy servers 312 repeatedly query task list502, obtain the next task therein, and perform this task until task list502 is empty or another stopping condition has been reached.

To facilitate discovery, proxy servers 312 may be configured withinformation regarding one or more subnets in managed network 300 thatare reachable by way of proxy servers 312. For instance, proxy servers312 may be given the IP address range 192.168.0/24 as a subnet. Then,customer instance 322 may store this information in CMDB 500 and placetasks in task list 502 for discovery of devices at each of theseaddresses.

FIG. 5A also depicts devices, applications, and services in managednetwork 300 as configuration items 504, 506, 508, 510, and 512. As notedabove, these configuration items represent a set of physical and/orvirtual devices (e.g., client devices, server devices, routers, orvirtual machines), applications executing thereon (e.g., web servers,email servers, databases, or storage arrays), relationshipstherebetween, as well as services that involve multiple individualconfiguration items.

Placing the tasks in task list 502 may trigger or otherwise cause proxyservers 312 to begin discovery. Alternatively or additionally, discoverymay be manually triggered or automatically triggered based on triggeringevents (e.g., discovery may automatically begin once per day at aparticular time).

In general, discovery may proceed in four logical phases: scanning,classification, identification, and exploration. Each phase of discoveryinvolves various types of probe messages being transmitted by proxyservers 312 to one or more devices in managed network 300. The responsesto these probes may be received and processed by proxy servers 312, andrepresentations thereof may be transmitted to CMDB 500. Thus, each phasecan result in more configuration items being discovered and stored inCMDB 500.

In the scanning phase, proxy servers 312 may probe each IP address inthe specified range of IP addresses for open Transmission ControlProtocol (TCP) and/or User Datagram Protocol (UDP) ports to determinethe general type of device. The presence of such open ports at an IPaddress may indicate that a particular application is operating on thedevice that is assigned the IP address, which in turn may identify theoperating system used by the device. For example, if TCP port 135 isopen, then the device is likely executing a WINDOWS® operating system.Similarly, if TCP port 22 is open, then the device is likely executing aUNIX® operating system, such as LINUX®. If UDP port 161 is open, thenthe device may be able to be further identified through the SimpleNetwork Management Protocol (SNMP). Other possibilities exist. Once thepresence of a device at a particular IP address and its open ports havebeen discovered, these configuration items are saved in CMDB 500.

In the classification phase, proxy servers 312 may further probe eachdiscovered device to determine the version of its operating system. Theprobes used for a particular device are based on information gatheredabout the devices during the scanning phase. For example, if a device isfound with TCP port 22 open, a set of UNIX®-specific probes may be used.Likewise, if a device is found with TCP port 135 open, a set ofWINDOWS®-specific probes may be used. For either case, an appropriateset of tasks may be placed in task list 502 for proxy servers 312 tocarry out. These tasks may result in proxy servers 312 logging on, orotherwise accessing information from the particular device. Forinstance, if TCP port 22 is open, proxy servers 312 may be instructed toinitiate a Secure Shell (SSH) connection to the particular device andobtain information about the operating system thereon from particularlocations in the file system. Based on this information, the operatingsystem may be determined. As an example, a UNIX® device with TCP port 22open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. Thisclassification information may be stored as one or more configurationitems in CMDB 500.

In the identification phase, proxy servers 312 may determine specificdetails about a classified device. The probes used during this phase maybe based on information gathered about the particular devices during theclassification phase. For example, if a device was classified as LINUX®,as a set of LINUX®-specific probes may be used. Likewise if a device wasclassified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probesmay be used. As was the case for the classification phase, anappropriate set of tasks may be placed in task list 502 for proxyservers 312 to carry out. These tasks may result in proxy servers 312reading information from the particular device, such as basicinput/output system (BIOS) information, serial numbers, networkinterface information, media access control address(es) assigned tothese network interface(s), IP address(es) used by the particular deviceand so on. This identification information may be stored as one or moreconfiguration items in CMDB 500.

In the exploration phase, proxy servers 312 may determine furtherdetails about the operational state of a classified device. The probesused during this phase may be based on information gathered about theparticular devices during the classification phase and/or theidentification phase. Again, an appropriate set of tasks may be placedin task list 502 for proxy servers 312 to carry out. These tasks mayresult in proxy servers 312 reading additional information from theparticular device, such as processor information, memory information,lists of running processes (applications), and so on. Once more, thediscovered information may be stored as one or more configuration itemsin CMDB 500.

Running discovery on a network device, such as a router, may utilizeSNMP. Instead of or in addition to determining a list of runningprocesses or other application-related information, discovery maydetermine additional subnets known to the router and the operationalstate of the router's network interfaces (e.g., active, inactive, queuelength, number of packets dropped, etc.). The IP addresses of theadditional subnets may be candidates for further discovery procedures.Thus, discovery may progress iteratively or recursively.

Once discovery completes, a snapshot representation of each discovereddevice, application, and service is available in CMDB 500. For example,after discovery, operating system version, hardware configuration andnetwork configuration details for client devices, server devices, androuters in managed network 300, as well as applications executingthereon, may be stored. This collected information may be presented to auser in various ways to allow the user to view the hardware compositionand operational status of devices, as well as the characteristics ofservices that span multiple devices and applications.

Furthermore, CMDB 500 may include entries regarding dependencies andrelationships between configuration items. More specifically, anapplication that is executing on a particular server device, as well asthe services that rely on this application, may be represented as suchin CMDB 500. For instance, suppose that a database application isexecuting on a server device, and that this database application is usedby a new employee onboarding service as well as a payroll service. Thus,if the server device is taken out of operation for maintenance, it isclear that the employee onboarding service and payroll service will beimpacted. Likewise, the dependencies and relationships betweenconfiguration items may be able to represent the services impacted whena particular router fails.

In general, dependencies and relationships between configuration itemsbe displayed on a web-based interface and represented in a hierarchicalfashion. Thus, adding, changing, or removing such dependencies andrelationships may be accomplished by way of this interface.

Furthermore, users from managed network 300 may develop workflows thatallow certain coordinated activities to take place across multiplediscovered devices. For instance, an IT workflow might allow the user tochange the common administrator password to all discovered LINUX®devices in single operation.

In order for discovery to take place in the manner described above,proxy servers 312, CMDB 500, and/or one or more credential stores may beconfigured with credentials for one or more of the devices to bediscovered. Credentials may include any type of information needed inorder to access the devices. These may include userid/password pairs,certificates, and so on. In some embodiments, these credentials may bestored in encrypted fields of CMDB 500. Proxy servers 312 may containthe decryption key for the credentials so that proxy servers 312 can usethese credentials to log on to or otherwise access devices beingdiscovered.

The discovery process is depicted as a flow chart in FIG. 5B. At block520, the task list in the customer instance is populated, for instance,with a range of IP addresses. At block 522, the scanning phase takesplace. Thus, the proxy servers probe the IP addresses for devices usingthese IP addresses, and attempt to determine the operating systems thatare executing on these devices. At block 524, the classification phasetakes place. The proxy servers attempt to determine the operating systemversion of the discovered devices. At block 526, the identificationphase takes place. The proxy servers attempt to determine the hardwareand/or software configuration of the discovered devices. At block 528,the exploration phase takes place. The proxy servers attempt todetermine the operational state and applications executing on thediscovered devices. At block 530, further editing of the configurationitems representing the discovered devices and applications may takeplace. This editing may be automated and/or manual in nature.

The blocks represented in FIG. 5B are for purpose of example. Discoverymay be a highly configurable procedure that can have more or fewerphases, and the operations of each phase may vary. In some cases, one ormore phases may be customized, or may otherwise deviate from theexemplary descriptions above.

V. EXAMPLE MACHINE LEARNING

Generally, machine learning (ML) relates to the ability of computers tolearn from and make predictions based on data. In practice, ML mayinclude a process of providing an ML algorithm with training data tolearn from, so as to create an ML model by a training process.Specifically, the ML algorithm may find pattern(s) in the training datathat map to a target variable (e.g., the answer an enterprise wants topredict) and may output an ML model that captures these pattern(s). Oncean ML model is outputted, ML may then involve using that ML model togenerate ML prediction(s) on new data for which the target variable isnot yet known.

By way of example, an ML platform could be provided with training datataking the form of electronic mails (e-mails) that have been previouslycategorized and with a target variable corresponding to determination ofcategories for uncategorized emails. As such, the ML platform could thenfind pattern(s) in the training data that map to that target variable,and may output an ML model accordingly. For instance, the ML platformmay determine that a relationship exists between times at which thecategorized e-mails were received and respective categories assigned tothose e-mails, and may then create an ML model according to thatrelationship. Once the ML model is created, the ML platform could usethat ML model to categorize other e-mails that have not yet beencategorized. Other examples are also possible.

VI. EXAMPLE SYSTEM TO FACILITATE SHARED MACHINE LEARNING

In line with the discussion above, disclosed herein is a network systemthat remotely facilitates generation of ML models and of ML predictionsfor various enterprise networks. In doing so, the network system couldsecurely generate ML models and corresponding ML predictions on percustomer instance basis. For example, a client device associated with aparticular customer instance may submit a request for the network systemto carry out a certain prediction and, once the network system generatesan ML model and a corresponding ML prediction according to that request,the generated ML model and ML prediction may accessible only to clientdevices associated with the particular customer instance. In this way,the network system could securely provide ML predictions that arespecific to an enterprise while helping that enterprise save computingresources and/or reduce costs on specialized software, among otherpossible outcomes.

FIG. 6 illustrates features, components, and operations of a networksystem that facilitates generation of ML models and of ML predictions.In particular, FIG. 6 illustrates a client device 600 as well as anetwork system including a computing system 602, a scheduler device 604,and a trainer device 606. Trainer device 606 may be one of a pluralityof trainer devices on the network system.

Although FIG. 6 illustrates a specific arrangement, it should beunderstood that various operations disclosed herein may be carried outin the context of similar and/or other arrangement(s) as well withoutdeparting from the scope of the present disclosure. Further, althoughthe present disclosure is described in the context of a remotemanagement network that remotely manages a managed network, it should beunderstood that aspects of the present disclosure may additionally oralternatively apply in other context(s) as well without departing fromthe scope of the present disclosure.

More specifically, FIG. 6 illustrates a client device 600, which may beone of the client devices 302 on the managed network 300. Generally, theclient device 600 may engage in communication with the computing system602, such as via wired and/or wireless communication link(s) (notshown). In this regard, the computing system 602 may be disposed withina remote network management platform, such as remote network managementplatform 320, so as to support remote management of the client device600's managed network.

Moreover, as shown, the client device 600 may be configured to operate aweb browser 608, which is a software application that may retrieve,present, and/or navigate through information on the World Wide Web. Thebrowser 608 may include a web-display tool (not shown) that provides foror otherwise supports display of information, such as informationreceived from the computing system 602. For example, as furtherdiscussed herein, the web-display tool may display information relatedto an ML prediction carried out by the network system. Other examplesare also possible.

Computing system 602 may include computing resources that enable use ofa customer instance 610 as discussed herein, which may be any one of theinstances of the managed network 300. Given this, the computing system602 may provide for some or all of the web portals, services, and/orapplications available to the client device 600's managed network,thereby supporting management of that managed network via customerinstance 610. And in accordance with the present disclosure, thecustomer instance 610 may include features that help carry out MLpredictions. Specifically, the customer instance 610 may include aprocessor 612, data storage 614, and a prediction ApplicationProgramming Interface (API) 616.

The processor 612 may be configured to coordinate operations within thecustomer instance 610 and to engage in various communications with theclient device 600, the scheduler device 604, and the trainer device 606.For example, the processor 612 may be configured to receive a “solutiondefinition” from the client device 600. As further discussed herein, thesolution definition may provide information designating certain data(e.g., data stored at the customer instance 610) as training data thatshould be used as basis for generating an ML model and may also provideinformation specifying a target variable to be predicted using the MLmodel. Additionally, the processor 612 may be configured to send an MLtraining request to the scheduler device 604, which, as furtherdiscussed herein, effectively triggers assignment of an ML trainerprocess to generate an ML model based on the the solution definition.Furthermore, the processor 612 may be configured to receive a generatedML model from the trainer device 606 and to store that ML model withinthe customer instance 610. Moreover, the processor 612 may be configuredto store, within the customer instance 610, an ML prediction that isbased on the ML model and to transmit the ML prediction to the clientdevice 600.

Data storage 614 may be configured to store data associated with thecustomer instance 610. For example, the data storage 610 may store anydata obtained and/or generated by the enterprise network of the clientdevice 600. In line with the present disclosure, at least a portion ofthat data could be designated as training data according to a solutiondefinition. In another example, the data storage 610 may store asolution definition received from a client device and/or an ML modelreceived from an ML trainer device. In yet another example, the datastorage 610 may store an ML prediction, such as by storing informationindicating a predicted target variable. Other examples are alsopossible.

Prediction API 616 may be configured to use ML model(s) to generate MLprediction(s). In practice, the prediction API 616 may be any currentlyavailable and/or future developed API arranged for the purpose ofgenerating various types of ML predictions. For example, the predictionAPI 616 could be specifically arranged to use ML model(s) to categorizean enterprise network's files, to determine priority of tasks listed inan enterprise network's task list, and/or to determine assignments forthose tasks (e.g., determine an enterprise's department that shouldcarry out the task), among others.

Further, scheduler device 604 may also be disposed within the remotenetwork management platform and may be configured to schedule theserving of ML training requests amongst a plurality of ML trainerdevices. The remote network management platform may include a pluralityof ML trainer devices each configured to execute one or more ML trainerprocesses, with each ML trainer process being configured to serve one MLtraining request at a time. Given this, the disclosed ML arrangementcould be a shared service, as each of a plurality of customer instancescould provide one or more ML training requests. Thus, the schedulerdevice 604 could coordinate the serving of those ML training requests byassigning an ML trainer process respectively to each ML trainingrequest, perhaps doing so based on one or more factors as furtherdiscussed herein.

By way of example, the scheduler device 604 could receive a first MLtraining request from a first computing system that enables use of afirst customer instance as well as a second ML training request from asecond computing system that enables use of a second customer instance.Responsively, the scheduler device 604 may assign the first ML trainingrequest to a first ML trainer process, which may cause a first MLtrainer device to execute the first ML trainer process serving the firstML training request, and may assign the second ML training request to asecond ML trainer process, which may cause a second ML trainer device toexecute the second ML trainer process serving the second ML trainingrequest.

In this example, the ML trainer devices and/or the ML trainer processcould be the same as or different from one another.

In one case, the scheduler device 604 may assign the first and second MLtraining requests to different ML trainer processes executed bydifferent ML trainer devices. Accordingly, in this case, the second MLtrainer device may be different from the first ML trainer device and thesecond ML trainer process may be different from the first ML trainerprocess. Moreover, the first and second ML trainer processes could berespectively assigned to serve the first and second ML training requestsat substantially the same time and/or at substantially different times.

In another case, the scheduler device 604 may assign the first andsecond ML training requests to different ML trainer processes executedby the same ML trainer device. Accordingly, in this case, the first andsecond ML trainer devices may be the same particular trainer device, butthe second ML trainer process may be different from the first ML trainerprocess. Here again, the first and second ML trainer processes could berespectively assigned to serve the first and second ML training requestsat substantially the same time and/or at substantially different times.

In yet another case, the scheduler device 604 may assign the first andsecond ML training requests to the same ML trainer process. Accordingly,in this case, the first and second ML trainer devices may be the sameparticular trainer device and the first and second ML trainer processesmay be the same particular ML trainer process. Moreover, in this case,the particular ML trainer process may be assigned to serve one MLtraining request at a time. For instance, the scheduler device 604 maybe configure to determine that the particular ML trainer process isavailable after completing serving of the first ML training request, andmay then responsively assign the second ML training request to theparticular ML trainer process. Other examples and cases are alsopossible.

To help schedule the serving of ML training requests amongst a pluralityof ML trainer devices, the scheduler device 604 may include a schedulingcontroller 618, a job queue 620, and a worker thread 622.

The scheduling controller 618 may be configured to initiate operationswithin the scheduler device 604 in response to receiving an ML trainingrequest. For example, the scheduling controller 618 may storeinformation related to a received ML training request, such as anidentifier of the customer instance from which the ML training requesthas been received and/or an identifier of a solution definition thatprovides basis for the ML training request, among others. In anotherexample, the scheduling controller 618 may create new ML training jobsin the job queue 620 feature based on received ML training requests.

The job queue 620 feature may include a listing of pending ML trainingjobs in accordance with ML training requests submitted by the computingsystem 602 and/or other computing system(s), which may include MLtraining requests that are yet to be served by an ML trainer processand/or ML training requests for which service is in-progress, amongother possibilities. Given this, the scheduling controller 618 couldcreate, based on a received ML training request, a new ML training jobin the job queue 622.

The worker thread 622 controller may be configured to manage ML trainingjobs listed in the job queue 620. For instance, the worker thread 622controller may inform a particular ML trainer device that a particularML training job is being assigned to a particular ML trainer processexecutable by the particular ML trainer device. When doing so, theworker thread 622 controller could also provide an identifier of theparticular customer instance associated with that particular ML trainingjob, so that the particular ML trainer device could engage incommunications with that particular customer instance as furtherdiscussed herein.

Yet further, ML trainer device 606 may be one of a plurality of MLtrainer devices disposed within the remote network management platform.Each such ML trainer device may be respectively configured to executeone or more ML trainer processes that can serve one or more ML trainingrequests by generating corresponding ML model(s). Moreover, in practice,some or all of the ML trainer devices could be at the same geographicallocation as one another and/or some or all of the ML trainer devicescould be at geographical locations that are different from one another.Nonetheless, a given ML trainer device, such as ML trainer device 606,may include a training controller 624, an executable ML trainer 626process, and temporary data storage 628.

The training controller 624 may be configured to initiate operationswithin the trainer device 606 as well as to engage in communication withthe computing system 602 and/or the scheduler device 604. For example,the training controller 624 may receive or otherwise pick up an MLtraining job from the scheduler device 604. In another example, thetraining controller 624 may receive and store information related to areceived ML training job (e.g., an identifier of the customer instancefrom which the corresponding ML training request has been received). Inyet another example, the training controller 624 may initiate theserving of an ML training request (corresponding to a received MLtraining job) by an ML trainer process, such as ML trainer 626 process.In yet another example, the training controller 624 may obtain trainingdata from a customer instance, such as customer instance 610, and maystore that training data in the temporary data storage 628. In yetanother example, the training controller 624 may determine a status of agiven ML training job, so that the training controller 624 can inform acustomer instance of that determined status. In yet another example,once an ML model has been generated, the training controller 624 mayprovide that ML model to a customer instance. Other examples are alsopossible.

In this regard, to facilitate determination of a status of a given MLtraining job, the training controller 624 may refer to the job queue 620and/or may query the ML trainer 626 process, among other possibilities.For example, if the training controller 624 determines that a given MLtraining job is listed in the job queue 620, then the trainingcontroller 624 may responsively determine that the ML training job ispending. In another example, if the training controller 624 determinesthat a given ML training job is being served by the ML trainer 626process, then the training controller 624 may responsively determinethat the ML training job is in-progress. In yet another example, if thetraining controller 624 determines that a given ML training job is nolonger in the job queue 620 and is no longer being served by the MLtrainer 626 process, then the training controller 624 may responsivelydetermine that the ML training job is complete. Other examples arepossible as well.

The ML trainer 626 process may take the form of any ML algorithm, code,routine or the like that is executable by the ML trainer device 606 tolearn from training data, so as to create an ML model by a trainingprocess. Examples of ML trainer processes may include (withoutlimitation): Decision Trees, Naïve Bayes Classification, Least SquaresRegression, and Logistic Regression, among others. As such, the MLtrainer 626 process may be any currently available and/or futuredeveloped ML trainer process arranged for the purpose of generatingvarious types of ML models. For example, the ML trainer 626 processcould be specifically arranged to generate ML model(s) that helpcategorize an enterprise network's files, that help determine priorityof tasks listed in an enterprise network's task list, and/or that helpdetermine assignments for those tasks, among others. Other examples arepossible as well.

The temporary data storage 628 may be configured to temporarily storetraining data. In particular, once the trainer device 606 obtainstraining data from the customer instance 610, the trainer device 606 maystore that training data in the temporary data storage 628 while the MLtrainer 626 process is serving a corresponding ML training request. Inthis way, the ML trainer 626 process could refer to the training datastored in the temporary data storage 628, so as to learn from thattraining data for the purpose of generating an ML model. However, oncethe trainer device 606 (e.g., the training controller 624) determinesthat the ML trainer 626 process completed the serving of thecorresponding ML training request, the trainer device 606 may delete thetraining data from the temporary data storage 628. As such, the trainerdevice 606 could store training data for each ML training request beingserved at the trainer device 606 and, once service of a given MLtraining request is complete, the trainer device 606 may delete thetraining data stored in association with that given ML training request.In this manner, due to the temporary storage of training data, thedisclosed ML arrangement helps secure an enterprise's data againstunauthorized access. Other arrangements are possible as well.

In a system arranged as described above, the client device 600, thecomputing system 602, the scheduler device 604, and/or the ML trainerdevice 606 may engage in various communications with one another. Inpractice, these communications may trigger one or more operations byrespective features/components of the client device 600, the computingsystem 602, the scheduler device 604, and/or the ML trainer device 606,such as operations described above with reference to FIG. 6, amongothers. Moreover, although particular communications are described in aparticular order, it should be understood that these communicationscould be carried out in any feasible order, that one or more of thesecommunications could be eliminated, and that one or more othercommunication could also be carried out to facilitate aspects of thepresent disclosure.

More specifically, the computing system 602 may receive a solutiondefinition 630 from the client device 600. Generally, the client device600 may transmit the solution definition 630 in response to receivinginput data (e.g., provided by a user) specifying the informationincluded in the solution definition 630. By way of example, the inputdata may be received via the browser 608 (e.g., via a graphical userinterface (GUI) displayed by the browser 608) and the browser 608 mayresponsively transmit the solution definition 630 to the processor 612as shown by FIG. 6.

In this regard, the solution definition 630 may include informationaccording to which the network system could ultimately generate an MLmodel and an ML prediction.

In particular, as noted, the solution definition 630 may provideinformation designating certain data as training data that should beused as basis for generating an ML model. For example, the solutiondefinition 630 may include a reference to specific data stored at thecustomer instance 610, so to designate that data as training data. In aspecific example, this reference could be a reference to particularcell(s), column(s), and/or row(s) within an electronic spreadsheet, suchas those that include previously categorized information, for instance.In another example, the solution definition 630 received from the clientdevice 600 may include the data that is the training data to be used asbasis for generating an ML model. In a specific example, the clientdevice 600 may send, to the processor 612 as part of the solutiondefinition 630, one or more files that include the training data. Otherexamples are also possible.

Additionally, as noted, the solution definition 630 may provideinformation specifying a target variable to be predicted using the MLmodel. For example, the target variable could relate to categorizationof information, prioritization of tasks, and/or determination of taskassignments, among others. In a specific example, the solutiondefinition 630 may include a reference to an empty column in anelectronic spreadsheet that is intended to specify respective categoriesfor uncategorized information listed in other portions of the electronicspreadsheet. In this example, the target variable thus relates tocategorization of uncategorized information in the electronicspreadsheet. Other examples are also possible.

In some cases, the solution definition 630 may also specify a type of MLtrainer process that should be used to generate an ML model. Forexample, the solution definition 630 could specify that one or more ofthe following ML trainer processes should be used: Decision Trees, NaïveBayes Classification, Least Squares Regression, and Logistic Regression.In this regard, the type of ML trainer process to be used for generatingan ML model could be selected, recommended, and/or otherwise determinedbased on various factor(s), such as based on preferences of the customerinstance, on the provided training data, and/or on the target variableto be determined, among other options. Other examples are also possible.

In yet other cases, the solution definition 630 may also specifytraining time(s) according to which the scheduler device 604 is toultimately assign the serving of corresponding ML training request(s).More specifically, the solution definition 630 could specify a singletraining time, multiple training times, and/or a training schedule,among other options.

In a specific example, the solution definition 630 could specify firstand second training times. As a result, the scheduler device 604 couldinitially receive a first ML training request based on the solutiondefinition 630 and could assign an ML trainer process to serve thatfirst ML training request at the first training time specified in thesolution definition 630, so as to generate an ML model. Then, thescheduler device 604 could receive a second ML training request based onthe same solution definition 630 and could assign an ML trainer processto serve that second ML training request at the second training timespecified in the solution definition 630, so as to generate an updatedML model, perhaps based on updated training data as further discussedherein.

In yet another example, the solution definition 630 could specify aperiodic training schedule. For instance, the solution definition 630could specify that the ML model should be updated once per day. As aresult, the scheduler device 604 could periodically receive ML trainingrequests based on the solution definition 630 and could assign MLtrainer process(es) to respectively serve those ML training requestsaccording to the periodic training schedule, so as to periodicallyupdate the ML model. Other examples are also possible.

Once the computing system 602 receives the solution definition 630 fromthe client device 600, the computing system 602 may responsively carryout certain operations. For example, the processor 612 may respond toreceiving the solution definition 630 by storing the solution definition630 at the data storage 614. Additionally, the processor 612 may respondto receiving the solution definition 630 by transmitting an ML trainingrequest 632 for reception by one of the plurality of trainer devices.Specifically, the processor 612 may transmit, to the schedulingcontroller 618, an ML training request 632 that is based on or otherwisecorresponds to the solution definition 630. In practice, the ML trainingrequest 632 may specify an identifier of the solution definition 630and/or an identifier of the customer instance 610, among others.

After the scheduling device 604 receives the ML training request 632from the computing system 602, the scheduling device 604 mayresponsively carry out certain operations to assign the ML trainingrequest 632 to a given one of the ML trainer processes. In particular,the scheduling controller 618 may respond to the ML training request 632by creating a new ML training job for the ML training request 632 in thejob queue 620 feature. In this way, the worker thread 622 controller mayultimately manage this ML training job.

When the worker thread 622 controller manages the ML training job, theworker thread 622 controller may send a “pick up job” message 634 to thetrainer device 606, which may indicate an assignment of the ML trainer626 process to the ML trainer job associated with the ML trainingrequest 632. In turn, this may effectively cause the ML trainer 626process to serve the ML training request 632. Moreover, the “pick upjob” message 634 could specify the identifier of the customer instance610 and/or the identifier of the solution definition 630, so that thetrainer device 606 could, as further discussed herein, obtain trainingdata 636 from the customer instance 610, provide a status update 638 tothe customer instance 610 and/or provide an ML model 640 to the customerinstance 610, among other options.

In this regard, when the scheduler device 604 assigns a particular oneof the network system's ML trainer processes to serve the ML trainingrequest 632, the scheduler device 604 could do so based on one or morefactors.

In one example, the scheduler device 604 may assign the ML trainingrequest 632 to an ML trainer process based on availability of the MLtrainer process. For instance, the scheduler device 604 may determinethat the ML trainer 626 process is available to serve the ML trainingrequest 632 (e.g., that the ML trainer 626 process is not currentlyserving any other ML training request). In practice, the schedulerdevice 604 could determine availability of the ML trainer 626 process byquerying the trainer device 606 and/or by maintaining and referring toan availability list (not shown), which may specify one or more MLtrainer processes and may indicate availability respectively of eachspecified ML trainer process, among other options. Nonetheless, once thescheduler device 604 makes a determination that the ML trainer 626process is available to serve the ML training request 632, the schedulerdevice 604 may assign the ML trainer 626 process to the ML trainingrequest 632 based on that determination.

In another example, the scheduler device 604 may assign the ML trainingrequest 632 to an ML trainer process based on consideration ofgeographical proximity of the ML trainer device executing the ML trainerprocess. In particular, the scheduler device 604 could make adetermination that a geographic location of the trainer device 606executing the ML trainer process 626 is threshold close to a geographiclocation of the computing system 602, and may assign the ML trainer 626process to the ML training request 632 based on that determination. Inone case, making this determination could involve determining that thegeographic location of the trainer device 606 executing the ML trainerprocess 626 is physically closest, from among corresponding geographiclocations of the plurality of ML trainer devices on the network system,to the geographic location of the computing system 602. In another case,making this determination could involve determining that a geographiclocation of the trainer device 606 executing the ML trainer process 626is within a threshold distance away from the geographic location of thecomputing system 602. In any case, the scheduler device 604 may assignan ML training process executable by a ML trainer device that isgeographically threshold close to (i.e., within a threshold of) acomputing system submitting a given ML training request, which may helpreduce or minimize network latency of subsequent communications betweenthe computing system and the ML trainer device executing the assigned MLtraining process, among other advantages.

In this example, the scheduler device 604 could use one of variousapproaches to determine a geographic location of any one of theplurality of ML trainer devices on the network system. For instance, thescheduler device 604 could maintain and refer to a “trainer devicelocations” list (not shown), which may specify one or more ML trainerprocesses and, for each given ML trainer process, may respectivelyindicate a geographic location of the ML trainer device configured toexecute that given ML trainer process.

Additionally, the scheduler device 604 could use one of variousapproaches to determine a geographic location of any one of thecomputing systems that respectively enable use of customer instances.For instance, the scheduler device 604 could maintain and refer to a“computing system locations” list (not shown), which may specify one ormore customer instances and, for each given customer instance, mayrespectively indicate a geographic location of the computing systemenabling use of that given customer instance.

In yet another example, the scheduler device 604 may assign the MLtraining request 632 to an ML trainer process based on consideration ofa topographical location of the ML trainer device executing the MLtrainer process. In particular, the scheduler device 604 could make adetermination that a topographical location of the trainer device 606executing the ML trainer process 626 is threshold close to the computingsystem 602, and may assign the ML trainer 626 process to the ML trainingrequest 632 based on that determination.

In this example, given a plurality of communication links respectivelybetween the computing system 602 and the plurality of trainer devices,the determination at issue could involve, for instance, determining thata communication link between the computing system 602 and the trainerdevice 606 provides for the fastest data transmission speed from amongthe data transmission speeds provided by the plurality of communicationlink. In another case, this determination could involve determining thatthe communication link between the computing system 602 and the trainerdevice 606 provides for a data transmission speed that is faster than athreshold speed. In any case, here again, the scheduler device 604 mayhelp reduce or minimize network latency of subsequent communicationsbetween the computing system and the ML trainer device executing theassigned ML training process, among other advantages.

In yet another example, the scheduler device 604 may assign the MLtraining request 632 to an ML trainer process based on consideration ofperformance metric(s) associated with ML trainer device(s). Inparticular, the scheduler device 604 may determine performance metric(s)respectively for each of one or more ML trainer device(s). Generally,performance metric(s) of a given ML trainer device may include (withoutlimitation): a memory usage level of the given ML trainer device,central processing unit (CPU) performance of the given ML trainerdevice, disk input/output (I/O) performance of the given ML trainerdevice, and/or network performance of the given ML trainer device, amongothers. Once the scheduler device 604 determines the performancemetric(s), the scheduler device 604 may assign the ML training request632 to an ML trainer process executable by an ML trainer device havingperformance metric(s) that meet a certain criteria.

For instance, the scheduler device 604 may assign the ML trainingrequest 632 to an ML trainer process executable by an ML trainer devicehaving performance metric(s) that are above or below certain performancethreshold(s). In a specific case, the scheduler device 604 may assignthe ML training request 632 to an ML trainer process executable by an MLtrainer device having a memory usage level that is lower than athreshold usage level. In another specific case, the scheduler device604 may assign the ML training request 632 to an ML trainer processexecutable by an ML trainer device having a memory usage level that islower than respective memory usage levels of one or more other MLtrainer devices being evaluated.

In some implementations, the scheduler device 604 could receiverecommendation(s) or may otherwise determine recommended ML trainerdevice(s) to which the scheduler device 604 could assign the ML trainingrequest 632. For instance, once the scheduler device 604 determinesperformance metric(s), the scheduler device 604 could determine aperformance score respectively for each of a plurality of ML trainerdevices. To do so for a given ML trainer device, the scheduler device604 could assign a weight respectively to each performance metricdetermined for that given ML trainer device, and could then determine aperformance score for the given ML trainer device according to aweighted average of these performance metrics. As such, once thescheduler device 604 determines a performance score respectively foreach of the plurality of ML trainer devices, the scheduler device 604could select one or more of these ML trainer devices as recommended MLtrainer devices based on certain criteria. For instance, the schedulerdevice 604 could select, as recommended ML trainer device(s), ML trainerdevice(s) that each respectively have a determined performance scorehigher than a threshold performance score. Accordingly, the schedulerdevice 604 may assign the ML training request 632 to an ML trainerprocess executable by one of the recommended ML trainer devices. Otherexamples are also possible.

Once the trainer device 606 picks up an ML training job (e.g., receivesthe “pick up job” message 634) from the scheduler device 604, thetrainer device 606 may then responsively carry out certain operations.

For instance, once the training controller 624 receives the “pick upjob” message 634, the training controller 624 may obtain the trainingdata 636 from the customer instance 610. To do so, the trainingcontroller 624 may transmit, to the customer instance 610, theidentifier of the customer instance 610 and/or the identifier of thesolution definition 630, which could be specified in the “pick up job”message 634 as noted above. In response to receiving the identifier ofthe customer instance 610 and/or the identifier of the solutiondefinition 630, the customer instance 610 may then provide, to thetraining controller 624, the training data 636 specified in the solutiondefinition 630, such as by providing a copy of the data designated astraining data 636 by the solution definition 630, among other options.The training controller 624 may then store the provided training data636 in the temporary data storage 628.

Moreover, after the training controller 624 receives the “pick up job”message 634, the training controller 624 may then facilitate executionof the ML trainer 626 process assigned to the ML training job associatedwith the ML training request 632. In doing so, the training controller624 may cause the ML trainer 626 process to serve the ML trainingrequest by generating an ML model 640 according to the solutiondefinition 630. Specifically, the ML trainer 626 process may learn fromthe training data 636 so as to generate an ML model 640 that could beused to predict the target variable indicated in the solution definition630.

Further, in line with the discussion above, the training controller 624could determine a status of the ML training job associated with the MLtraining request 632, so that the training controller 624 can inform thecustomer instance 610 of that determined status. As such, the trainingcontroller 624 may transmit, to the processor 612, a status update 638indicating the status of the ML training job associated with the MLtraining request 632. The processor 612 could then transmit that statusupdate 638 to the client device 600, such as for display by the browser608, for instance. Moreover, when the training controller 624 provides astatus update, the training controller 624 could do so upon request(e.g., sent by the client device 600 to the trainer device 606 via thecomputing system 602) and/or according to a schedule, among otheroptions.

Yet further, once the trainer device 606 generates the ML model 640, thetrainer device 606 may send the generated ML model 640 to the customerinstance 610. In doing so, the trainer device 606 could also include theidentifier of the customer instance 610 and/or the identifier of thesolution definition 630. In this way, the customer instance 610 coulduse one or more of these identifiers to determine that the provided MLmodel 640 is associated with the solution definition 630 originallyreceived from the client device 600. In this regard, once the customerinstance 610 receives the ML model 640, the customer instance 610 (e.g.,the processor 612) may store the ML model 640 in the data storage 614,so that the customer instance 610 could refer to this ML model 640 atany time.

Once the computing system 602 receives and stores the ML model 640, thecomputing system 602 may then predict the target variable indicated inthe solution definition 630 using the ML model 640. In particular, theprediction API 616 may obtain the ML model 640 from the data storage 614and may then use the ML model 640 to generate an ML prediction 642, suchas by outputting the target variable indicated in the solutiondefinition 630. For example, in line with the examples above, the targetvariable could relate to categorization of uncategorized information inthe electronic spreadsheet. As such, in this example, the ML prediction642 may include a prediction of categories for the uncategorizedinformation in the spreadsheet or may otherwise involve an actualcategorization of that previously uncategorized information in thespreadsheet, among other options. Other examples are also possible.

In this regard, the disclosed arrangement may allow the computing system602 to carry out offline prediction(s). In particular, in line with thediscussion above, the disclosed arrangement provides for MLprediction(s) to be carried out separately from the ML model generation,specifically being carried out by the computing system 602. As a result,the computing system 602 could feasibly generate the ML prediction 642at any time as long as the computing system 602 has the ML model 640stored thereon. For example, the prediction API 616 could use the MLmodel 640 stored in the data storage 614 to predict the target variableindicated in the solution definition 630, and could do so even if thecomputing system 602 doesn't have an established network connection withany one of the trainer devices.

Nonetheless, after the computing system 602 generates the ML prediction642, the computing system 602 may then provide the ML prediction 642 tothe client device 600. In one case, the prediction API 616 may store theML prediction 642 in the data storage 614, and the processor 612 mayobtain the ML prediction 642 from the data storage 614 and may thentransmit the ML prediction 642 to the client device 600. In anothercase, the processor 612 may obtain the ML prediction 642 directly fromthe prediction API 616 and may then transmit the ML prediction 642 tothe client device 600. In either case, when the processor 612 transmitsthe ML prediction 642 to the client device 600, the processor 612 mayprovide information indicating the target variable. For example, theprocessor 612 may provide information indicating categories determinedrespectively for each of a plurality of previously uncategorized files.

Moreover, once the client device 600 receives the ML prediction 642 fromthe computing system 602, the client device 600 may responsively presentthat ML prediction 642 in some manner. For example, the browser 608 mayuse the above-mentioned web-display tool to display informationindicating the target variable, such as by displaying graphics, text,numbers, and/or other characters representative of the target variable.In another example, the client device 600 may use an audio output deviceto output an audible notification representative of the target variable.Other examples are also possible.

In a further aspect, the disclosed arrangement could allow for use thatsame ML model to carry out multiple prediction(s). For instance, theclient device 600 could effectively request a prediction by providingthe solution definition 630 to the computing system 602, and may thenreceive the prediction 642 as discussed. Then, the client device 600could request the computing system 602 to generate and provide anotherprediction using that same ML model 640, and the computing system 602could do so accordingly. In a specific example, once the computingsystem 602 has an ML model arranged for predicting a target variablerelated to categorizing files, the computing system 602 may use the MLmodel to categorize one set of previously uncategorized files. Then, theclient device 600 could request the computing system 602 to use that MLmodel to categorize another set of previously uncategorized filed, andthe computing system 602 could do so accordingly. Other examples arealso possible.

In yet a further aspect, the disclosed arrangement could allow forprediction of multiple target variables. For instance, the computingsystem could receive information indicating first training data, secondtraining data, a first target variable to be predicted using a first MLmodel, as well as a second target variable to be predicted using asecond ML model. In this case, the computing system could transmit firstand second ML training requests, so that the requests are respectivelyreceived for service by first and second ML trainer processes in linewith the discussion above. Here again, the first and second ML trainerprocesses could be the same as or different from one another. Also, thefirst and second ML trainer processes could be respectively executed byfirst and second trainer devices, which could be the same as ordifferent from one another. Further, the first and second training datacould be the same as or different from one another. Moreover, the firstand second ML trainer processes could respectively serve the first andsecond ML training requests at substantially the same time or atdifferent times.

In any case, the first trainer device executing the first ML trainerprocess could provide the computing system with a first ML model that isgenerated based on the first training data and according to the first MLtrainer process, and the computing system could then predict the firsttarget variable using the first ML model and could transmit informationindicating the first target variable to a client device. Similarly, thesecond trainer device executing the second ML trainer process couldprovide the computing system with a second ML model that is generatedbased on the second training data and according to the second ML trainerprocess, and the computing system could then predict the second targetvariable using the second ML model and could transmit informationindicating the second target variable to a client device.

In yet a further aspect, in line with the discussion above, thedisclosed arrangement could allow for generating an updated ML model andfor using that updated ML model to carry out additional prediction(s).In particular, the computing system 602 could send another ML trainingrequest for reception by one of the plurality of trainer devices, andcould do so in response to obtaining updated training data and/oraccording to training times specified by the solution definition 630,among other options. Additionally, when an ML trainer device is servingthe other ML training request, the computing system 602 could providethe updated training data to that ML trainer device, so that the MLtrainer device could generate an updated ML model based on the updatedtraining data. Once the computing system 602 then receives the updatedML model from the trainer device, the computing system 602 could thenuse that updated ML model to generate additional prediction(s) andprovide those prediction(s) to the client device 600. For instance, thecomputing system 602 could use the updated ML model to again predict thetarget variable indicated in the solution definition 630, and thecomputing system 602 could the transmit, to the client device 600,update information indicating the target variable predicted using theupdated ML model.

In this regard, the particular ML trainer process generating the updatedML model could be the same as or different from the ML trainer 626process that generated the original ML model 640. If the particular MLtrainer process generating the updated ML model is different from the MLtrainer 262 process that generated the original ML model 640, thatparticular ML trainer process could be executable by the same ML trainerdevice 606 that is also configured to execute the ML trainer 262 processor could be executable by a different ML trainer device.

Furthermore, the computing system 602 could obtain updated training datain various ways. For example, the client device 600 may send, to thecomputing system 602, an update to the solution definition 630, whichmay include a new reference to other data stored at the customerinstance 610, so to designate that data as additional or alternativetraining data to be used for be used as basis for generating an updatedML model. In a specific example, this new reference could be a referenceto additional or alternative cell(s), column(s), and/or row(s) withinthe above-mentioned electronic spreadsheet, such as those that includeother previously categorized information, for instance. In anotherexample, the client device 600 may send, to the computing system 602,new data designated as training data that should additionally oralternatively be used as basis for generating an updated ML model. In aspecific example, the client device 600 may send, to the processor 612,one or more additional files that include additional training data.Other examples are also possible.

VII. ADDITIONAL SECURITY FEATURE

In yet a further aspect, the disclosed arrangement may provide asecurity feature that may further help secure an enterprise network'sdata. In particular, an ML trainer device could provide a secureidentifier to a computing system when obtaining training data from thecomputing system, so that the computing system could verify that the MLtraining device is permitted to obtain the training data. In practice,the secure identifier may be a randomly generated bitstring, such as asecurity token cryptographically generated by the computing system.However, other secure identifiers are possible as well without departingfrom the scope of the present disclosure.

By way of example, to help facilitate this security feature, thecomputing system may transmit a randomly generated bitstring along withthe ML training request for reception by one of the plurality of trainerdevices, such as for reception by the scheduler device. Once thescheduler device then assigns an ML trainer process to serve the MLtraining request, the scheduler device may transmit the randomlygenerated bitstring to the ML trainer device configured to execute theassigned ML trainer process. Then, once the ML trainer device seeks toobtain training data from the computing system, the ML trainer devicemay send the randomly generated bitstring to the computing system, suchas along with a request for the training data. As such, the computingsystem may verify that the randomly generated bitstring received fromthe ML trainer device is identical to the randomly generated bitstringoriginally transmitted by the computing system. And once the computingsystem completes this verification process, the computing system mayresponsively provide the training data to the ML trainer device.

VIII. EXAMPLE APPLICATION OF SHARED MACHINE LEARNING

In practice, the disclosed shared ML arrangement could be used byenterprise(s) or the like for a variety of applications. One example ofsuch an application could involve ML predictions related to remainingdisk space of an enterprise network. Based on the received MLpredictions related to remaining disk space of the enterprise network,an enterprise could then make operational decisions, such as advanceinvestment in additional disk space for the enterprise network, amongother options.

By way of example, the computing system 602 could receive a solutiondefinition indicating training data and a target variable in line withthe discussion above. In this example, the training data could be aplurality of data points each indicating an extent of remaining diskspace at a respective point in time. Additionally, the target variablecould correspond to a request to predict a point in time at which theenterprise network will run out of disk space. Tables 1 and 2 belowrepresent an example of such a solution definition.

TABLE 1 X (Time) Janu- Febru- ary ary March April May June July (1) (2)(3) (4) (5) (6) (7) Y 2.5 2.1 2.0 1.5 1.5 1.3 0.9 (Remaining Disk Spacein Terabytes (TB))

TABLE 2 Target Variable Value(X) when Y = 0

Specifically, Table 1 shows training data corresponding to data pointsthat indicate extent of remaining disk space respectively at each ofvarious months of a given year. As shown, the remaining disk space isrepresented by the variable Y and the month is represented by thevariable X. For instance, Table 1 shows that the enterprise network has2.5 TB of remaining disk space in January (i.e., 1^(st) month of theyear), that the enterprise network has 2.1 TB of remaining disk space inFebruary (i.e., 2^(nd) month of the year), that the enterprise networkhas 2.0 TB of remaining disk space in March (i.e., 3^(rd) month of theyear), and so on. Moreover, Table 2 shows a target variablecorresponding to a request to predict a month at which the enterprisenetwork will run out of disk space (e.g., a month at which theenterprise network will have 0 TB of disk space remaining).

Yet further, as noted, the solution definition could specify a type ofML trainer process that should be used to generate an ML model. Forinstance, in this example, the solution definition could specify use oflinear regression techniques. As such, a trainer device may ultimatelygenerate an ML model according to the specified type, such as byexecuting an ML trainer process that relies on linear regressiontechniques. Other features of the solution definition are possible aswell.

Once the computing system 602 receives this solution definition, thecomputing system 602 may carry out the operations described in thecontext of FIG. 6, so as to obtain an ML model from the trainer device606. In this example, the trainer device 606 could generate the ML modelby executing an ML trainer process (e.g., ML trainer 626 process) thatrelies on linear regression techniques. As a result, the trainer device606 could use the training data shown in Table 1 to generate a ML modelthat indicates the following Equation 1:Y=−0.2464X+2.671   Equation 1

Once the computing system 602 receives the generated ML model, thecomputing system 602 may carry out the operations described in thecontext of FIG. 6, so as to generate a prediction using the received MLmodel. In particular, the computing system 602 may predict a month atwhich the enterprise network will run out of disk space. To do so, thecomputing system 602 may insert a value of zero (0) into the variable Yof the above-mentioned Equation 1 and may then solve for the value of X.In this example, when the value of zero (0) is inserted into thevariable Y of Equation 1, the resulting value of X is 10.84, whichcorresponds to the month of October (i.e. 10^(th) month of the year). Assuch, the computing system 602 may predict that the enterprise networkwill run out of disk space sometime in the month of October, and couldprovide this prediction to a client device of the enterprise network.

Moreover, in line with the discussion above, the computing system 602could obtain an updated ML model based on updated training data and maythen use that updated ML model to carry out another prediction. Forinstance, the computing system 602 could obtain another data pointindicating 0.8 TB of remaining disk space in the month of August (8).Subsequently, the computing system 602 may carry out the operationsdescribed in the context of FIG. 6, so as to obtain an updated ML modelfrom the trainer device 606. In this example, the trainer device 606could generate the updated ML model by again executing the ML trainerprocess that relies on linear regression techniques. As a result, thetrainer device 606 could use the training data shown in Table 1 alongwith the newly obtained data point to generate an updated ML model thatindicates the following Equation 2:Y=−0.2381X+2.646   Equation 2

Once the computing system 602 receives the updated ML model, thecomputing system 602 may carry out the operations described in thecontext of FIG. 6, so as to generate a new prediction using the receivedupdated ML model. In particular, the computing system 602 may againpredict a month at which the enterprise network will run out of diskspace. To do so, the computing system 602 may insert a value of zero (0)into the variable Y of the above-mentioned Equation 2 and may then solvefor the value of X. In this example, when the value of zero (0) isinserted into the variable Y of Equation 2, the resulting value of X is11.11, which corresponds to the month of November (i.e. 11^(th) month ofthe year). As such, the computing system 602 may newly predict that theenterprise network will run out of disk space sometime in the month ofNovember, and could provide this new prediction to a client device ofthe enterprise network. Other examples are also possible.

IX. EXAMPLE OPERATIONS

FIG. 7 is a flow chart illustrating an example embodiment. The processillustrated by FIG. 7 may be carried out by a computing system, such ascomputing device 100, and/or a cluster of computing devices, such asserver cluster 200. However, the process can be carried out by othertypes of devices or device subsystems. For example, the process could becarried out by a portable computer, such as a laptop or a tablet device.

The embodiments of FIG. 7 may be simplified by the removal of any one ormore of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theprevious figures or otherwise described herein.

Block 702 may involve receiving, by a computing system of a remotenetwork management platform, information indicating (i) training datathat is associated with the computing system and that is to be used asbasis for generating a machine learning (ML) model and (ii) a targetvariable to be predicted using the ML model, where the information isreceived from a client device of a managed network, where the remotenetwork management platform remotely manages the managed network, wherea plurality of trainer devices are disposed within the remote networkmanagement platform, and where each trainer device is configured toexecute one or more ML trainer processes.

Block 704 may involve transmitting, by the computing system, an MLtraining request for reception by one of the plurality of trainerdevices, where the ML training request is based on the receivedinformation.

Block 706 may involve providing, by the computing system, the trainingdata to a particular trainer device executing a particular ML trainerprocess that is serving the ML training request.

Block 708 may involve receiving, by the computing system from theparticular trainer device, the ML model that is generated based on theprovided training data and according to the particular ML trainerprocess.

Block 710 may involve predicting, by the computing system, the targetvariable using the ML model.

Block 712 may involve transmitting, by the computing system to theclient device, information indicating the target variable.

In some embodiments, transmitting the ML training request for receptionby one of the plurality of trainer devices comprises transmitting the MLtraining request to a scheduler device for scheduling of the ML trainingrequest, where the scheduler device assigns the ML training request tothe particular ML trainer process. Generally, the scheduler device maybe disposed within the remote network management platform and may beconfigured to schedule service of ML training requests amongst theplurality of trainer devices.

In some embodiments, the scheduler device may be further configured tomake a determination that a location of the particular trainer device isthreshold close to a location of the computing system. In theseembodiments, the scheduler device may assign the ML training request tothe particular ML trainer process based at least on the determinationthat the location of the particular trainer device is threshold close toa location of the computing system.

In some embodiments, the scheduler device may be further configured tomake a determination that the particular ML trainer process is availableto serve the ML training request. In these embodiments, the schedulerdevice may assign the ML training request to the particular ML trainerprocess based at least on the determination that the particular MLtrainer process is available to serve the ML training request.

In some embodiments, the computing system may be a first computingsystem, the ML training request may be a first ML training request, theparticular trainer device may be a first trainer device, the particularML trainer process may be a first ML trainer process, and the schedulerdevice may be further configured to: receive, from a second computingsystem disposed within the remote network management platform, a secondML training request for scheduling of the second ML training request;and, in response to receiving the second ML request, assign the secondML training request to a second ML trainer process, where assignment ofthe second ML training request to the second ML trainer process causes asecond trainer device to execute the second ML trainer process servingthe second ML training request.

In such embodiments, the second trainer device may be different from thefirst trainer device and the second ML trainer process may be differentfrom the first ML trainer process, the first and second trainer devicesmay be the same particular trainer device and the second ML trainerprocess may be different from the first ML trainer process, or the firstand second trainer devices may be the same particular trainer device andthe first and second ML trainer processes may be the same particular MLtrainer process.

Moreover, in a situation in which the first and second trainer devicesare the same particular trainer device and the first and second MLtrainer processes are the same particular ML trainer process, then thescheduler device may be further configured to determine that theparticular ML trainer process is available after completing serving ofthe first ML training request. In this case, assigning the second MLtraining request to the particular ML trainer process is further inresponse to determining that the particular ML trainer process isavailable after completing serving of the first ML training request.

In some embodiments, the information received from the client device mayspecify a training time, and the scheduler device assigning the MLtraining request to the particular ML trainer process may involve thescheduler device assigning the particular ML trainer process to servethe ML training request at the specified training time.

In some embodiments, the computing system may be further configured to:transmit a randomly generated bitstring along with the ML trainingrequest for reception by one of the plurality of trainer devices;receive the randomly generated bitstring from the particular trainerdevice when the particular trainer device requests that the computingsystem provide the training data; verify that the randomly generatedbitstring received from the particular trainer device is identical tothe randomly generated bitstring transmitted by the computing system;and in response to the verifying, provide the training data to theparticular trainer device.

In some embodiments, the particular trainer device may include atemporary data storage device and the particular trainer device may beconfigured to: store the training data at the temporary data storagedevice while the particular ML trainer process is serving the MLtraining request; and delete the training data from the temporary datastorage device after the particular ML trainer process completes theserving of the ML training request.

In some embodiments, the ML training request may be a first ML trainingrequest, the particular trainer device may be a first trainer device,the particular ML trainer process may be a first ML trainer process, thetarget variable may be a first target variable, the ML model may be afirst ML model, and the received information may also indicate (i)second training data that is associated with the computing system andthat is to be used as basis for generating a second ML model and (ii) asecond target variable to be predicted using the second ML model. Insuch embodiments, the computing system may be further configured to: (i)transmit a second ML training request for reception by one of theplurality of trainer devices, wherein the second ML training request isalso based on the received information; (ii) provide the second trainingdata to a second trainer device executing a second ML trainer processthat is serving the second ML training request; (iii) receive, from thesecond trainer device, the second ML model that is generated based onthe training data and according to the second ML trainer process; (iv)predict the second target variable using the second ML model; and (v)transmit, to the client device, information indicating the second targetvariable.

In some embodiments, the computing system may include a data storagedevice and may be configured to: store the receive ML model at the datastorage device; and use the stored ML model to predict the targetvariable without the computing system having an established networkconnection to any one of the plurality of trainer devices.

In some embodiments, a web browser may operated by the client device,and transmitting, to the client device, information indicating thetarget variable may involve causing the web browser to display theinformation indicating the target variable.

In some embodiments, the ML training request may be a first ML trainingrequest, the particular trainer device may be a first trainer device,the particular ML trainer process may be a first ML trainer process, thefirst ML trainer process may serving the first ML training request at afirst training time, and the computing system is further configured to:transmit a second ML training request for reception by one of theplurality of trainer devices, where the second ML training request isalso based on the received information; provide updated training data toa second trainer device executing a second ML trainer process that isserving the second ML training request, wherein the second ML trainerprocess is serving the second ML training request at a second trainingtime after the first training time; receive, from the second trainerdevice, an updated ML model that is generated based on the updatedtraining data and according to the second ML trainer process; predictthe target variable using the updated ML model; and transmit, to theclient device, updated information indicating the target variablepredicted using the updated ML model.

In such embodiments, the first and second trainer devices may be thesame particular trainer device and the second ML trainer process may bedifferent from the first ML trainer process. Alternatively, the firstand second trainer devices may be the same particular trainer device andthe first and second ML trainer processes may be the same particular MLtrainer process.

X. CONCLUSION

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its scope, as will be apparent to thoseskilled in the art. Functionally equivalent methods and apparatuseswithin the scope of the disclosure, in addition to those describedherein, will be apparent to those skilled in the art from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims.

The above detailed description describes various features and operationsof the disclosed systems, devices, and methods with reference to theaccompanying figures. The example embodiments described herein and inthe figures are not meant to be limiting. Other embodiments can beutilized, and other changes can be made, without departing from thescope of the subject matter presented herein. It will be readilyunderstood that the aspects of the present disclosure, as generallydescribed herein, and illustrated in the figures, can be arranged,substituted, combined, separated, and designed in a wide variety ofdifferent configurations.

With respect to any or all of the message flow diagrams, scenarios, andflow charts in the figures and as discussed herein, each step, block,and/or communication can represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, operationsdescribed as steps, blocks, transmissions, communications, requests,responses, and/or messages can be executed out of order from that shownor discussed, including substantially concurrently or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or operations can be used with any of the message flow diagrams,scenarios, and flow charts discussed herein, and these message flowdiagrams, scenarios, and flow charts can be combined with one another,in part or in whole.

A step or block that represents a processing of information cancorrespond to circuitry that can be configured to perform the specificlogical functions of a herein-described method or technique.Alternatively or additionally, a step or block that represents aprocessing of information can correspond to a module, a segment, or aportion of program code (including related data). The program code caninclude one or more instructions executable by a processor forimplementing specific logical operations or actions in the method ortechnique. The program code and/or related data can be stored on anytype of computer readable medium such as a storage device including RAM,a disk drive, a solid state drive, or another storage medium.

The computer readable medium can also include non-transitory computerreadable media such as computer readable media that store data for shortperiods of time like register memory and processor cache. The computerreadable media can further include non-transitory computer readablemedia that store program code and/or data for longer periods of time.Thus, the computer readable media may include secondary or persistentlong term storage, like ROM, optical or magnetic disks, solid statedrives, compact-disc read only memory (CD-ROM), for example. Thecomputer readable media can also be any other volatile or non-volatilestorage systems. A computer readable medium can be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a step or block that represents one or more informationtransmissions can correspond to information transmissions betweensoftware and/or hardware modules in the same physical device. However,other information transmissions can be between software modules and/orhardware modules in different physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purpose ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A network system comprising: a computing systemdisposed within a remote network management platform, the remote networkmanagement platform comprising a scheduler device and a plurality oftrainer devices, wherein each trainer device is configured to executeone or more machine learning (ML) trainer processes, wherein thecomputing system is configured to: receive information comprising:training data associated with the computing system, wherein the trainingdata is to be used as basis for generating an ML model; and a targetvariable to be predicted using the ML model; wherein the information isreceived from a client device of a managed network, and wherein theremote network management platform remotely manages the managed network;based on the received information, transmit an ML training request tothe scheduler device, wherein the scheduler device assigns the MLtraining request to a particular trainer device of the plurality oftrainer devices to execute a particular ML trainer process of theplurality of ML trainer processes, wherein the scheduler device providesan instance identifier of the particular computational instance to theparticular trainer device, wherein the instance identifier enablesdirect communication between the particular trainer device and theparticular computational instance; receive a secure identifier from theparticular trainer device; verify that the particular trainer device ispermitted to obtain the training data; in response to verifying that theparticular trainer device is permitted to obtain the training data,provide the training data to the particular trainer device executing theparticular ML trainer process that is serving the ML training request;receive, from the particular trainer device, the ML model that isgenerated based on the provided training data and according to theparticular ML trainer process; predict the target variable using the MLmodel; and transmit, to the client device, information indicating thetarget variable.
 2. The network system of claim 1, wherein the schedulerdevice is configured to schedule service of ML training requests amongstthe plurality of trainer devices.
 3. The network system of claim 2,wherein the scheduler device is configured to: make a determination thata location of the particular trainer device is threshold close to alocation of the computing system, and wherein the scheduler deviceassigns the ML training request to the particular ML trainer processbased at least on the determination.
 4. The network system of claim 2,wherein the scheduler device is configured to: make a determination thatthe particular ML trainer process is available to serve the ML trainingrequest, and wherein the scheduler device assigns the ML trainingrequest to the particular ML trainer process based at least on thedetermination.
 5. The network system of claim 2, wherein the computingsystem is a first computing system, wherein the ML training request is afirst ML training request, wherein the particular trainer device is afirst trainer device, and wherein the particular ML trainer process is afirst ML trainer process, the network system further comprising: asecond computing system disposed within the remote network managementplatform, wherein the scheduler device is further configured to:receive, from the second computing system, a second ML training requestfor scheduling of the second ML training request; and in response toreceiving the second ML request, assign the second ML training requestto a second ML trainer process, wherein assignment of the second MLtraining request to the second ML trainer process causes a secondtrainer device to execute the second ML trainer process serving thesecond ML training request.
 6. The network system of claim 5, whereinthe second trainer device is different from the first trainer device,and wherein the second ML trainer process is different from the first MLtrainer process.
 7. The network system of claim 5, wherein the first andsecond trainer devices are the same particular trainer device, andwherein the second ML trainer process is different from the first MLtrainer process.
 8. The network system of claim 5, wherein the first andsecond trainer devices are the same particular trainer device, andwherein the first and second ML trainer processes are the sameparticular ML trainer process.
 9. The network system of claim 8, whereinthe scheduler device is configured to: determine that the particular MLtrainer process is available after completing serving of the first MLtraining request, and wherein assigning the second ML training requestto the particular ML trainer process is further in response todetermining that the particular ML trainer process is available aftercompleting serving of the first ML training request.
 10. The networksystem of claim 2, wherein the information received from the clientdevice specifies a training time, and wherein the scheduler deviceassigning the ML training request to the particular ML trainer processcomprises the scheduler device assigning the particular ML trainerprocess to serve the ML training request at the specified training time.11. The network system of claim 1, wherein the computing system isconfigured to: transmit a randomly generated bitstring along with the MLtraining request to one of the plurality of trainer devices; whereinreceiving the secure identifier from the particular trainer devicecomprises receiving the randomly generated bitstring from the particulartrainer device when the particular trainer device requests that thecomputing system provide the training data; and wherein verifying thatthe particular trainer device is permitted to obtain the training datacomprises verifying that the randomly generated bitstring received fromthe particular trainer device is identical to the randomly generatedbitstring transmitted by the computing system.
 12. The network system ofclaim 1, wherein the particular trainer device comprises a temporarydata storage device, and wherein the particular trainer device isconfigured to: store the training data at the temporary data storagedevice while the particular ML trainer process is serving the MLtraining request; and delete the training data from the temporary datastorage device after the particular ML trainer process completes theserving of the ML training request.
 13. The network system of claim 1,wherein the ML training request is a first ML training request, whereinthe particular trainer device is a first trainer device, wherein theparticular ML trainer process is a first ML trainer process, wherein thetraining data is first training data, wherein the target variable is afirst target variable, wherein the ML model is a first ML model, whereinthe received information comprises: second training data associated withthe computing system, wherein the second training data is to be used asbasis for generating a second ML model; and a second target variable tobe predicted using the second ML model, and wherein the computing systemis configured to: transmit a second ML training request to one of theplurality of trainer devices, wherein the second ML training request isalso based on the received information; provide the second training datato a second trainer device executing a second ML trainer process that isserving the second ML training request; receive, from the second trainerdevice, the second ML model that is generated based on the training dataand according to the second ML trainer process; predict the secondtarget variable using the second ML model; and transmit, to the clientdevice, information indicating the second target variable.
 14. Thenetwork system of claim 1, wherein the computing system comprises a datastorage device, and wherein the computing system is configured to: storethe receive ML model at the data storage device; and use the stored MLmodel to predict the target variable without the computing system havingan established network connection to any one of the plurality of trainerdevices.
 15. The network system of claim 1, wherein a web browser isoperated by the client device, and wherein transmitting, to the clientdevice, information indicating the target variable comprises causing theweb browser to display the information indicating the target variable.16. The network system of claim 1, wherein the ML training request is afirst ML training request, wherein the particular trainer device is afirst trainer device, wherein the particular ML trainer process is afirst ML trainer process, wherein the first ML trainer process isserving the first ML training request at a first training time, andwherein the computing system is further configured to: transmit a secondML training request to one of the plurality of trainer devices, whereinthe second ML training request is also based on the receivedinformation; provide updated training data to a second trainer deviceexecuting a second ML trainer process that is serving the second MLtraining request, wherein the second ML trainer process is serving thesecond ML training request at a second training time after the firsttraining time; receive, from the second trainer device, an updated MLmodel that is generated based on the updated training data and accordingto the second ML trainer process; predict the target variable using theupdated ML model; and transmit, to the client device, updatedinformation indicating the target variable predicted using the updatedML model.
 17. The network system of claim 16, wherein the first andsecond trainer devices are the same particular trainer device, andwherein the second ML trainer process is different from the first MLtrainer process.
 18. The network system of claim 16, wherein the firstand second trainer devices are the same particular trainer device, andwherein the first and second ML trainer processes are the sameparticular ML trainer process.
 19. A method comprising: receiving, by acomputing system of a remote network management platform, informationcomprising: training data associated with the computing system, whereinthe training data is to be used as basis for generating a machinelearning (ML) model; and a target variable to be predicted using the MLmodel; wherein the information is received from a client device of amanaged network, wherein the remote network management platform remotelymanages the managed network, wherein a scheduler device and a pluralityof trainer devices are disposed within the remote network managementplatform, and wherein each trainer device is configured to execute oneor more ML trainer processes; based on the received information,transmitting, by the computing system, an ML training request to thescheduler device, wherein the scheduler device assigns the ML trainingrequest to a particular trainer device of the plurality of trainerdevices to execute a particular ML trainer process of the plurality ofML trainer processes, wherein the scheduler device provides an instanceidentifier of the particular computational instance to the particulartrainer device, wherein the instance identifier enables directcommunication between the particular trainer device and the particularcomputational instance; receiving, by the computing system, a secureidentifier from the particular trainer device; verifying, by thecomputing system, that the particular trainer device is permitted toobtain the training data; in response to verifying that the particulartrainer device is permitted to obtain the training data, providing, bythe computing system, the training data to the particular trainer deviceexecuting the particular ML trainer process that is serving the MLtraining request; receiving, by the computing system from the particulartrainer device, the ML model that is generated based on the providedtraining data and according to the particular ML trainer process;predicting, by the computing system, the target variable using the MLmodel; and transmitting, by the computing system to the client device,information indicating the target variable.
 20. An article ofmanufacture including a non-transitory computer-readable medium, havingstored thereon program instructions that, upon execution by a computingsystem of a remote network management platform, cause the computingsystem to perform operations, comprising: receiving informationcomprising: training data associated with the computing system, whereinthe training data is to be used as basis for generating a machinelearning (ML) model; and a target variable to be predicted using the MLmodel; wherein the information is received from a client device of amanaged network, wherein the remote network management platform remotelymanages the managed network, wherein a scheduler device and a pluralityof trainer devices are disposed within the remote network managementplatform, and wherein each trainer device is configured to execute oneor more ML trainer processes; based on the received information,transmitting an ML training request to the scheduler device, wherein thescheduler device assigns the ML training request to a particular trainerdevice of the plurality of trainer devices to execute a particular MLtrainer process of the plurality of ML trainer processes, wherein thescheduler device provides an instance identifier of the particularcomputational instance to the particular trainer device, wherein theinstance identifier enables direct communication between the particulartrainer device and the particular computational instance; receiving, bythe computing system, a secure identifier from the particular trainerdevice; verifying, by the computing system, that the particular trainerdevice is permitted to obtain the training data; in response toverifying that the particular trainer device is permitted to obtain thetraining data, providing the training data to the particular trainerdevice executing the particular ML trainer process that is serving theML training request; receiving, from the particular trainer device, theML model that is generated based on the provided training data andaccording to the particular ML trainer process; predicting the targetvariable using the ML model; and transmitting, to the client device,information indicating the target variable.